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Automatic License Plate Recognition is related to the Intelligent Transportation System (ITS) that supports the road's e-law enforcement system. In the case of the Indonesian license plate, with various colour rules for font and background, and sometimes vehicle owners modify their license plate font format, this is a challenge in the image processing approach. This research utilizes pre-trained of AlexNet, VGGNet, and ResNet to determine the optimum model of Indonesian character license plate recognition. Three pre-trained approaches in CNN-based detection for reducing time for a build if model from scratch. The experiment shows that using the pre-trained ResNet model gives a better result than another two approaches. The optimum results were obtained at epoch 50 with an accuracy of 99.9% and computation time of 26 minutes. This experiment results fulfil the goal of this research. Keywords : ALPR; ITS; CNN; AlexNet; VGGNet; ResNet

These the problem of parking systems on the streets is a classic problem that occurs from year to year, many solutions are offered in solving parking problems on the street. The problem is not only related to congestion due to exit and enter the vehicle from the parking lot but also the problem of parking management becomes the current polemic. Optimization of integrated parking management system tries to provide solution in parking management that is by giving real time vehicle parking data to server so that manager can access data then make report or policy related parking. This system can be implemented multi-parking area in an integrated manner. In addition, this system can also provide vehicle parking monitoring for security vehicles in the parking lot. The development of this system for parking vehicle identification is supported by smart camera. In the prototype, the system test is carried out in day and night conditions, to anticipate the difference of pixel intensity on the vehicle object then in developing vehicle detection program using adaptive thresholding method. The results show that the identification vehicle program and sending data in real time from smart camera to server have been running quite well during the day and night.

The existence of level crossings between railroads and road vehicles that do not have gates in areas far from crowds, such conditions require gates that are made automatically to avoid accidents. The Automated Railroad Crossing System (ARCS) is an automatically activated railroad crossing gate where train arrival information is obtained through sensors. For one level crossing, there are several electronic devices installed in the automatic railroad crossing system. It is planned that the automatic railroad crossing system will be installed at several level crossings. The problem is how to estimate the time to perform automatic railroad crossing maintenance at several different locations, For this reason, it is necessary to know the estimated remaining useful life (Remaining Useful Life) of the subsystems. The purpose of this research is to find the estimated remaining useful time (RUL) of the subsystem in the automatic railroad crossing system in order to estimate the time to perform maintenance. The process that is carried out to obtain the remaining useful time is through the Prognostic Health Management System development plan, while the analysis of the estimated remaining useful time is carried out using Principle Component Analysis (PCA), the results of this simulation show promising results to determine the estimated value of the remaining useful time. If it can be known the estimated remaining time of the benefit, it is hoped that the maintenance plan for each automatic railroad crossing system can be carried out more efficiently.